DSSG helps not-for-profit organisations and government bodies to achieve more with their data by enhancing their services, interventions and outreach, helping fulfil their mission of improving the world and people’s lives.
In 2021, Warwick is organising DSSGx UK Summer Projects - a 12-week Fellowship programme that trains students to create industry-standard data science products in collaboration with government agencies and NGOs, to deliver positive social impact.
The programme gives not-for-profit organisations and government bodies unprecedented access to inspiring, top-tier data science talent. This helps build their capacity to use cutting-edge quantitative methods to address societal challenges in areas such as education, health, energy, public safety, transportation and economic development.
DSSGx Summer Projects are delivered by the University of Warwick in collaboration with Ludwig-Maximilians-Universität in Munich under the DSSGx UK chapter of the DSSG Foundation.
The DSSG Foundation’s core summer Fellowship was set up in 2013 by Rayid Ghani, former Chief Scientist for President Obama. The US Fellowship is based at Carnegie Mellon University.
DSSGx - The UK programme
In 2019, the University of Warwick collaborated with The Alan Turing Institute to bring DSSG to the UK as an affiliate programme of the DSSG Foundation’s core summer Fellowship at Carnegie Mellon University. It ran in 2019 at the University of Warwick, and online in 2020 due to the Covid-19 pandemic.
In 2021, Warwick will deliver DSSGx UK Summer Projects in collaboration with Ludwig-Maximilians-Universität in Munich, and supported by The Alan Turing Institute and the DSSG Foundation.
DSSGx UK 2021 will take place from 7 June – 27 August, as an entirely online event. Given the situation, the 2021 programme will provide a different kind of training experience than usual, and focus on providing more time for hands-on collaboration with our mentors in implementing project goals.
This year, we'll use the R programming language, specifically the mlr3 ecosystem implemented in R. mlr3 offers basic functions for machine learning, but also extensions for preprocessing, pipelining, visualisation and other tasks. Strong coding experience is a prerequisite, preferably with R experience (or at least eagerness to learn it).
Projects from previous years
Ofsted - Risk assessing foster homes
The goal of the project was to use a range of data both held by Ofsted and publicly available to build a risk model that identifies foster homes for children that are at risk of providing sub-standard care.
This risk assessment needed to fit within Ofsted‘s decision making process and help inform Ofsted’s decisions about when to inspect providers with the primary aim of increasing the number of children receiving a better quality of care and education.
World Bank – Identifying and analysing corruption risks in public administration
A collaboration with the World Bank investigated how cutting-edge data science methodologies can link public procurement data and the asset declarations of public officials to support the identification and analysis of corruption risks in public administration. This will allow practitioners, policy makers and civil society to inform policy responses and address corruption risks in the public sector.
The increasing availability of machine-readable open data generated by governments is improving the ability to analyse and understand corruption risks. The initiative will help address the gap of analytical frameworks and data driven methods that are needed. It will explore how big data and machine learning methodologies can support the analysis of the scale, mechanics and impact of corrupt practices within public administration to help increase accountability and integrity in the public sector. The DSSG project will contribute to the World Bank’s broader anticorruption research programme and the technical advice it provides to governments.
Homeless Link – Prioritising alerts
Homeless Link have an app called StreetLink so anybody can send them alerts from their mobile with a geo-tag when they spot a rough sleeper. Alerts are reviewed by volunteers to see if there is sufficient information to send local service out to help and support. But with up to 1,000 alerts a day during winter, it can take days for volunteers to review them all, by which time the rough sleeper has moved on, with just 14% being found by local services.
DSSGx has worked with Homeless Link and built a data science model to review and prioritise the alerts more quickly, so services can find more rough sleepers and more quickly.
Work with us
For undergraduate or postgraduates at a higher education institute, DSSGx provides and unrivalled opportunity to spend the summer working with an international team of data scientists on a project with real social impact.
Teams will be supported by technical mentors and project managers, and alongside the project runs a training programme that will be flexibly adapted to cover the needs of the team and the project.
If you have a passion for data science, care about the social good and want to work in an international team, we would welcome your application.
This is an intense full-time internship. All selected Fellows will receive a stipend covering their living costs and expenses.
Applications for fellows are now closed.
We're looking for sponsors to work with our projects and third sector partners, as well as core sponsors that share our vision, aim and values. The programme attracts incredible talent and supports them to deliver high impact, data driven projects for social good. Sponsors continue to benefit from the reputation of the programme and alignment with project goals long after the summer programme ends.
To learn more, please contact email@example.com
If you're a non-profit or government organisation with a data science challenge, you can apply to become a project partner.
We're seeking projects that aim to tackle an important problem with high social impact that can be addressed with data and expertise provided by our partners.
You will need to support the team working on your project approximately one day per week.
By the end of the project, we aim to have delivered a software solution ready to be used in your organisation.
If you're interested in becoming a project partner, please apply here.
Here's what previous partners have said about us:
"Becoming a project partner with DSSG presented us with a unique and exciting opportunity to analyse our StreetLink data and develop models to improve our systems - and ultimately provide better outcomes for people sleeping rough."
Gareth Thomas, Senior Information Manager, Homeless Link
"The professionalism and expertise of the team working on Ofsted’s project was impressive. Their dedication, not only to deliver a high quality solution, but also to understand how Ofsted works was vital to make the project a success."
James Bowsher-Murray, Head of Early Years and Social Care - Data and Insight, Ofsted
DSSGx is seeking paid, full-time technical mentors and project managers for 2021 (three months, fixed-term).
- Mentors typically have a strong technical background, often a PhD in data science, computer science, statistics, social science or public policy, plus several years of industry experience.
- Project managers have a strong technical background and exposure to technology or data science projects, and several years of industry or consulting experience.
As mentor or project manager you'll work with top international data science talent on projects that really matter.
To register interest, please contact firstname.lastname@example.org
As a volunteer
We are looking for socially-minded volunteers with data science experience to lend their time and expertise to help deliver DSSGx 2021.
For more information and to put yourself forward please contact email@example.com
University of Warwick
- Juergen Branke, Warwick Business School, University of Warwick
- Sebastian Vollmer, Statistics, University of Warwick
- Colm Connaughton, Mathematics, University of Warwick
- Graham Cormode, Computer Science, University of Warwick
- Marya Bazzi, Mathematics, University of Warwick
Ludwig-Maximilians-Universität in Munich
- Bernd Bischl, Statistics , Ludwig-Maximilians-Universität in Munich
- Frauke Kreuter, Statistics, Ludwig-Maximilians-Universität in Munich
Susanne Dandl, Statistics , Ludwig-Maximilians-Universität in Munich
Florian Pfisterer, Statistics , Ludwig-Maximilians-Universität in Munich
Christoph Kern, Social Sciences, University of Mannheim
To register your interest or to find out more about how you can get involved, please contact firstname.lastname@example.org
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